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• W2116747293 abstract "This paper forms a part of a series of studies we have undertaken, in which the problem of adaptive Volterra system identification is examined. We assume that we have an observed output signal derived from a Volterra filter that is driven by a Gaussian input. Both the filter parameters and the input signal are unknown and therefore the problem can be classified as blind or unsupervised in nature. In the statistical approach to the solution of the above problem we formulate equations that relate the unknown parameters of the Volterra model with the statistical parameters of the output signal. These equations are highly nonlinear and their solution is achieved through a constrained optimisation formulation. We attempt to interpret the constrained problem as an unconstrained one by incorporating various types of the so called functions. The barrier function (or interior point) methods are believed to have properties which are theoretically or computationally desirable for constrained optimisation. The results of the entire modelling scheme are compared with other contributions." @default.
• W2116747293 created "2016-06-24" @default.
• W2116747293 creator A5028031376 @default.
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• W2116747293 date "2002-11-23" @default.
• W2116747293 modified "2023-09-25" @default.
• W2116747293 title "Blind adaptive Volterra system identification using barrier function methods for constrained optimisation" @default.
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• W2116747293 doi "https://doi.org/10.1109/acssc.1997.680019" @default.
• W2116747293 hasPublicationYear "2002" @default.
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